Free Practice Questions for Microsoft DP-600 Exam

To reduce memory usage and refresh time, splitting the OrderDateTime into separate date and time columns (A) can help optimize the model because date/time data types can be more memory-intensive than separate date and time columns. Moreover, replacing TotalSalesAmount with a measure (D) instead of a calculated column ensures that the calculation is performed at query time, which can reduce the size of the model as the value is not stored but calculated on the fly. References = The best practices for optimizing Power BI models are detailed in the Power BI documentation, which recommends using measures for calculations that don ' t need to be stored and adjusting data types to improve performance.

Comprehensive Detailed Explanation

Step 1: Understanding the requirement

We need a KQL query that:

Reads data from Weatherdata.

Orders by Datetime.

Calculates Delta_temperature as the difference between the current row and the preceding row.

Returns the final columns: Datetime, Lat, Long, Temperature, Delta_temperature.

Step 2: Evaluate the provided statements

Weatherdata → Starts the query with the source table.

| sort by Datetime asc → Ensures chronological order, required for prev() to work correctly.

| extend Delta_temperature = Temperature - prev(Temperature) → Computes difference between the current row and the previous row’s temperature.

| project Datetime, Lat, Long, Temperature, Delta_temperature → Selects the required output columns.

Step 3: Incorrect choices (to avoid)

next(Temperature,1) → Would calculate the difference with the next row, not the previous.

prev(Temperature,2) → Would skip one row, giving the difference with two rows back, which is not needed.

summarize … by Datetime → Aggregates the data; not required here since we just need row-level differences.

Step 4: Final Correct Sequence

Weatherdata

| sort by Datetime asc

| extend Delta_temperature = Temperature - prev(Temperature)

| project Datetime, Lat, Long, Temperature, Delta_temperature

References

prev() function in KQL

extend operator in KQL

project operator in KQL

Requirement

Fabric workspace: Workspace1

Contains: Warehouse1, Model1, Report1

Task: Allow User1 to access a single table in Warehouse1.

Must follow principle of least privilege → grant only the exact permissions required.

Step 1: Evaluate Options

A. Assign object level permissions to User1 for Warehouse1.

In Fabric warehouses, you can grant permissions at the object level (table, schema, or column).

If the requirement is access to only one table, the correct approach is to grant SELECT permissions on that specific table.

This satisfies least privilege.

Correct. ✅

B. Assign the db_datareader role to User1 for Warehouse1.

db_datareader provides read access to all tables in the database/warehouse.

This violates least privilege.

Not correct.

C. Share Warehouse1 with User1.

Sharing grants access to the whole warehouse.

Too broad, not least privilege.

Not correct.

D. Assign the Viewer role to User1 for Workspace1.

Viewer role allows seeing all items in the workspace (warehouse, model, reports).

This would expose more than the single table.

Not correct.

Step 2: Correct Action

Use object-level permissions:

GRANT SELECT ON dbo.TableName TO [User1];

This ensures User1 can only query that specific table, nothing else.

References

Manage object-level security in Microsoft Fabric warehouses

Principle of least privilege in Fabric

You should complete the code as follows:

Table

NativeQuery

EnableFolding

In Power Query, using Table before the SQL statement ensures that the result of the SQL query is treated as a table. NativeQuery allows a native database query to be passed through from Power Query to the source database. The EnableFolding option ensures that any subsequent transformations that can be folded will be sent back and executed at the source database (Microsoft SQL Server engine in this case).

Step 1 – Select the required fields and count

We want to group by CustomerAltKey and count occurrences.

SELECT CustomerAltKey, COUNT(*)

Step 2 – From the table

FROM DimCustomer

Step 3 – Grouping by email

GROUP BY CustomerAltKey

Step 4 – Filtering duplicates (only values with count > 1)

HAVING COUNT(*) > 1

Step 5 – Limiting the result to 1,000 rows

LIMIT 1000

(or in T-SQL, SELECT TOP(1000) ...). Since both options are shown, we use LIMIT 1000 because Fabric Warehouse supports Synapse SQL T-SQL + ANSI SQL style.

Correct Sequence:

SELECT CustomerAltKey, COUNT(*)

FROM DimCustomer

GROUP BY CustomerAltKey

HAVING COUNT(*) > 1

LIMIT 1000

When creating a OneLake shortcut to Amazon S3, authentication uses the standard AWS access key ID + secret access key pair.

SAS token is for Azure Storage, not AWS.

Certificate thumbprint is not used for S3.

Access ID is incorrect naming; the correct term is access key ID.

Correct answers: B and D.